A capsule network includes layers of capsules, each consisting of a group of neurons representing different properties of the input. The mechanism to learn interactions between capsules is performed with routing algorithms such as dynamic routing [1]}. Capsules can share the knowledge across locations using a pose matrix that represents the relation between parts of an object and the whole object, thus making capsules capable of dealing with viewpoint changes. Accordingly, the activation of a capsule is also based on a comparison between multiple incoming pose predictions. Capsule networks have recently been adopted in the context of face analysis tasks, most notably in age and gender classification [2]}, and action detection [3]}. This paper proposes the combination of a capsule network with a CNN to, respectively, learn the available inter-view and intra-view relations available in an LF image for face and expression recognition. In this context, the novel CapsField solution not only learns viewpoint-invariant relations between different parts of a face and the whole face in each view, but also it learns the relations between the different views along the whole multi-view sequence.
A capsule network is also capable of learning feature importance by assigning higher weights to the more relevant features, while ignoring the spurious dimensions [4]}. This functionality can act as an attention mechanism in the proposed CapsField solution, selectively focusing on the most important angular features making the solution more robust to changes in appearance and environment.
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